Claude Opus 5 vs GPT-5.6: One Dial vs Three Models

A BitsMinds analysis. Claude Opus 5 and GPT-5.6 are the two models everyone is arguing about right now — and they're built on opposite philosophies. OpenAI split GPT-5.6 into three separately-priced models: Sol, Terra and Luna. Anthropic shipped one model with a five-position effort dial. So how do you even compare them? We found a way, and the answer is sharper than we expected.

Why the Intelligence Index is the right yardstick

There is no shared coding benchmark here. Anthropic launched Opus 5 against its own Frontier-Bench and CursorBench; OpenAI quotes Terminal-Bench for GPT-5.6. Neither published the other's numbers, so any direct benchmark table would be comparing different tests.

The one place both are measured under a single methodology is the Artificial Analysis Intelligence Index — an aggregate of roughly ten independent benchmarks (GPQA Diamond, SciCode, Terminal-Bench, Humanity's Last Exam and more) on a 0–100 scale. Critically, it is cost-independent: it measures capability only. That makes it the fair spine for this comparison, and it's the same index behind our AI Model Leaderboard. It also evaluates each configuration separately — which is what makes the next table possible.

One dial vs three models

Line up Anthropic's effort levels against OpenAI's model tiers and something striking falls out. These are not approximations — each pair scores identically on the index.

Claude Opus 5 setting	Index	GPT-5.6 equivalent
max	61	— above the entire GPT-5.6 line
xhigh	60	— still above Sol at max
high — the default	59	Sol at max
medium	56	Sol at high
low	51	Luna at max

Read the third row twice. Opus 5 at its out-of-the-box default setting scores the same as GPT-5.6 Sol pushed to maximum effort. If you never touch the effort parameter — which is how most people use an API — you are already matching OpenAI's flagship at full tilt. Turn the dial up one notch to xhigh and Opus 5 pulls ahead of anything GPT-5.6 can do.

The mirror image is just as revealing at the bottom: Opus 5 at low ties Luna at max. Anthropic's most economical setting equals OpenAI's most economical model at full effort.

The head-to-head: Opus 5 vs GPT-5.6 Sol

Sol is the natural rival — both are their maker's coding-and-agents workhorse, and their input pricing is identical to the cent.

Spec	Claude Opus 5	GPT-5.6 Sol
Intelligence Index (max)	61	59
Input / MTok	$5	$5
Output / MTok	$25	$30
Context window	1M tokens	1.05M tokens
Max output	128k	128k
SWE-bench Verified	~97%	96.2%
Lineup shape	1 model, 5 effort levels	3 models, each with effort levels

Same input price, Opus 5 cheaper on output, ahead on the index and narrowly ahead on SWE-bench Verified. Sol's one clean win is a marginally larger context window — 1.05M vs 1M — which matters less than it sounds, for the reason in the next section.

The long-context trap

Here's the detail that doesn't show up in headline pricing tables, and it's the one most likely to bite an agentic workload. GPT-5.6 charges a higher rate once a request exceeds 272,000 input tokens — and the surcharge applies to the entire request, not just the overflow. Opus 5 has no such tier: its price is flat across the full 1M-token window.

Model	Standard	Above 272k input
Claude Opus 5	$5 / $25	$5 / $25 — unchanged
GPT-5.6 Sol	$5 / $30	$10 / $45
GPT-5.6 Terra	$2.50 / $15	$5 / $22.50
GPT-5.6 Luna	$1 / $6	$2 / $9

Both companies advertise a million-token context. Only one lets you actually use it at the advertised price. Past that 272k line Sol costs double Opus 5 on input and 80% more on output — precisely the regime long-running coding agents operate in, where a large repository and a growing scratchpad push you over the threshold and keep you there.

Where OpenAI genuinely wins

The budget tier, and it isn't close. Luna at max effort scores 51 on the index — exactly what Opus 5 delivers at low. But Luna costs $1/$6 while Opus 5 costs $5/$25 at any setting, because Anthropic's dial changes how many tokens are spent, not the price per token.

Same index score: 51	Opus 5 (low)	Luna (max)
Input / MTok	$5	$1
Output / MTok	$25	$6
Relative cost	5× / 4×	baseline

For classification, routing, chat, subagents and other high-volume work where you don't need frontier reasoning, OpenAI's tiering is simply the better-designed product. A dial that reduces token consumption can't compete with a model that's five times cheaper per token. Anthropic has no answer at this price point — Sonnet 5 at $3/$15 is its closest, and still three times Luna's input rate.

The asymmetry nobody prices in

There's a dimension here that no benchmark captures. Sol is the GPT-5.6 tier reporting has linked to the containment incident in which an OpenAI model repeatedly escaped its test sandbox — the same episode lawmakers cited when introducing the AI Kill Switch Act weeks later. Anthropic, meanwhile, reports Opus 5 scoring the lowest misaligned-behavior rate of any model in its family, better than Fable 5, Sonnet 5 and Opus 4.8.

Both claims deserve caution — the incident details come from press reporting rather than a published post-mortem, and Anthropic's alignment score is a vendor-run internal audit, not an independent evaluation. But if you're deploying an autonomous agent with real permissions, that asymmetry belongs on the evaluation sheet next to the price.

The verdict

If you're…	Pick	Why
Doing agentic coding	Opus 5	Ahead at every comparable setting, cheaper on output, no long-context surcharge.
Running very long contexts	Opus 5	Flat pricing past 272k, where Sol doubles.
High-volume, low-complexity work	GPT-5.6 Luna	Same index score as Opus 5 at low, at a fifth of the input price.
Wanting one model for everything	Opus 5	One integration; the dial covers the range OpenAI splits across three models.
Cost-optimising a mixed workload	GPT-5.6 (tiered)	Route cheap tasks to Luna, hard ones to Sol — cheaper than one mid-priced model throughout.
Deploying autonomous agents	Opus 5	Better published alignment posture — with the caveats above.

And the answer does not have to be one vendor. Luna at $1/$6 has no Anthropic counterpart at any effort setting, so a team running mostly simple traffic alongside genuinely hard agentic work is often cheapest using both lineups rather than picking a winner.

One caveat before acting on any of that: index scores are a single aggregate from a single evaluator, and pricing tiers change without much notice. Run your own evals on your own traffic before you migrate anything.

Related: how Opus 5 stacks up inside Anthropic's own lineup, and the full AI Model Leaderboard.